Tool-Augmented Dialogue Architecture for Scenario-Adaptive Responses
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Solution Overview
Problem
Existing Task-Oriented Dialogue Systems (TODS) struggle to accurately understand user needs and provide personalized, diversified responses across various application scenarios, leading to inefficiencies in user engagement and satisfaction.
Innovation Solution
A method and apparatus that utilize a tool large language model trained through supervised fine-tuning with historical dialogue data and pluggable tools, enabling deep understanding of user needs and generating targeted responses by aligning intelligent agent characters and language styles to specific scenarios, while allowing quick adaptation to multiple application contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a modular architecture with four main modules (NLU, DST, DPL, NLG) is used for task-oriented dialogue systems, then the system can complete specific user tasks through multiple rounds of dialogue, but the system struggles to accurately understand user needs and provide personalized, diversified responses across various application scenarios
Solution Approach 1:
The patent applies universality by designing a tool selection framework that enables a single dialogue system to perform multiple functions across different application scenarios. The system selectively invokes different tools (search, calculation, recommendation, etc.) based on the detected scenario type, allowing one system to handle diverse tasks like e-commerce shopping, travel planning, and information querying without requiring separate specialized systems for each scenario.
Solution Approach 2:
The patent implements dynamics through the scenario detection mechanism that dynamically adjusts system behavior based on input characteristics. The system continuously monitors dialogue context, detects the current application scenario, and dynamically selects appropriate tools and response strategies. This dynamic adaptation enables the system to transition between different operational modes (e.g., from information retrieval to transaction processing) based on real-time user needs.
2Productivity
If traditional supervised learning or reinforcement learning methods are used for dialogue policy learning, then the system can learn from historical data, but it cannot quickly adapt to multiple application contexts and provides limited personalized responses
Solution Approach 1:
The patent introduces scenario detection as an intermediary component between the dialogue understanding module and the tool selection module. This intermediary analyzes the dialogue context, identifies the application scenario, and mediates the selection of appropriate tools and response strategies. This intermediary layer enables the system to bridge general dialogue processing with scenario-specific responses, improving both personalization and efficiency.
Solution Approach 2:
The patent applies parameter changes by adjusting system behavior parameters based on detected scenarios. Different scenarios trigger different parameter configurations, such as varying the priority of certain tools, modifying response generation strategies, or changing the depth of dialogue state tracking. This parameter-based adaptation allows the system to optimize performance for each specific application context while maintaining a unified architecture.
3Ease of manufacture
If end-to-end generation is used for natural language generation, then the system can generate responses efficiently, but it lacks the capability to invoke external tools to process user query information and user data
Solution Approach 1:
The patent segments the response generation process into distinct stages: dialogue understanding, scenario detection, tool selection, tool execution, and response generation. This segmentation allows the system to invoke external tools at the appropriate stage (after scenario detection and before final response generation) without compromising the efficiency of the overall process. Each segment handles a specific aspect, maintaining clarity and manageability while enabling sophisticated tool-based processing.
Data Source
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AI summary
The present disclosure provides a method and apparatus for dialogue, relates to the field of artificial intelligence technology, in particular to the field of natural language processing and deep learning technology, and can be used in application scenarios such as generative search, intelligent editing of documents, intelligent assistants, virtual assistants, or intelligent e-commerce. A specific embodiment of the method includes: determining an application scenario corresponding to user query information; acquiring user data in the application scenario; invoking a tool in the application scenario, to process the user query information and the user data to obtain a tool execution result; and generating, based on the tool execution result, answer information corresponding to the user query information.